Overview

Dataset statistics

Number of variables35
Number of observations100
Missing cells368
Missing cells (%)10.5%
Duplicate rows3
Duplicate rows (%)3.0%
Total size in memory28.1 KiB
Average record size in memory288.0 B

Variable types

Numeric26
Categorical9

Alerts

ERC20_avg_time_between_sent_tnx has constant value ""Constant
ERC20_avg_time_between_rec_tnx has constant value ""Constant
ERC20_avg_time_between_rec_2_tnx has constant value ""Constant
ERC20_avg_time_between_contract_tnx has constant value ""Constant
ERC20_min_val_sent_contract has constant value ""Constant
ERC20_max_val_sent_contract has constant value ""Constant
ERC20_avg_val_sent_contract has constant value ""Constant
Dataset has 3 (3.0%) duplicate rowsDuplicates
Avg_min_between_sent_tnx is highly overall correlated with max_val_sent and 1 other fieldsHigh correlation
Avg_min_between_received_tnx is highly overall correlated with Time_Diff_between_first_and_last_(Mins)High correlation
Time_Diff_between_first_and_last_(Mins) is highly overall correlated with Avg_min_between_received_tnx and 4 other fieldsHigh correlation
min_value_received is highly overall correlated with avg_val_received and 11 other fieldsHigh correlation
max_value_received is highly overall correlated with avg_val_received and 4 other fieldsHigh correlation
avg_val_received is highly overall correlated with min_value_received and 5 other fieldsHigh correlation
min_val_sent is highly overall correlated with min_value_received and 8 other fieldsHigh correlation
max_val_sent is highly overall correlated with Avg_min_between_sent_tnx and 7 other fieldsHigh correlation
avg_val_sent is highly overall correlated with min_value_received and 5 other fieldsHigh correlation
total_Ether_sent is highly overall correlated with Avg_min_between_sent_tnx and 7 other fieldsHigh correlation
total_ether_received is highly overall correlated with max_value_received and 3 other fieldsHigh correlation
Total_ERC20_tnxs is highly overall correlated with Time_Diff_between_first_and_last_(Mins) and 13 other fieldsHigh correlation
ERC20_total_Ether_received is highly overall correlated with min_value_received and 7 other fieldsHigh correlation
ERC20_total_ether_sent is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_uniq_sent_addr is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_uniq_rec_addr is highly overall correlated with Time_Diff_between_first_and_last_(Mins) and 8 other fieldsHigh correlation
ERC20_uniq_rec_contract_addr is highly overall correlated with Time_Diff_between_first_and_last_(Mins) and 8 other fieldsHigh correlation
ERC20_max_val_rec is highly overall correlated with min_value_received and 6 other fieldsHigh correlation
ERC20_avg_val_rec is highly overall correlated with min_value_received and 6 other fieldsHigh correlation
ERC20_min_val_sent is highly overall correlated with ERC20_total_ether_sent and 4 other fieldsHigh correlation
ERC20_max_val_sent is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_avg_val_sent is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_uniq_sent_token_name is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_uniq_rec_token_name is highly overall correlated with Time_Diff_between_first_and_last_(Mins) and 8 other fieldsHigh correlation
ERC20_total_Ether_sent_contract is highly imbalanced (90.7%)Imbalance
ERC20_uniq_sent_addr.1 is highly imbalanced (90.7%)Imbalance
Total_ERC20_tnxs has 16 (16.0%) missing valuesMissing
ERC20_total_Ether_received has 16 (16.0%) missing valuesMissing
ERC20_total_ether_sent has 16 (16.0%) missing valuesMissing
ERC20_total_Ether_sent_contract has 16 (16.0%) missing valuesMissing
ERC20_uniq_sent_addr has 16 (16.0%) missing valuesMissing
ERC20_uniq_rec_addr has 16 (16.0%) missing valuesMissing
ERC20_uniq_sent_addr.1 has 16 (16.0%) missing valuesMissing
ERC20_uniq_rec_contract_addr has 16 (16.0%) missing valuesMissing
ERC20_avg_time_between_sent_tnx has 16 (16.0%) missing valuesMissing
ERC20_avg_time_between_rec_tnx has 16 (16.0%) missing valuesMissing
ERC20_avg_time_between_rec_2_tnx has 16 (16.0%) missing valuesMissing
ERC20_avg_time_between_contract_tnx has 16 (16.0%) missing valuesMissing
ERC20_min_val_rec has 16 (16.0%) missing valuesMissing
ERC20_max_val_rec has 16 (16.0%) missing valuesMissing
ERC20_avg_val_rec has 16 (16.0%) missing valuesMissing
ERC20_min_val_sent has 16 (16.0%) missing valuesMissing
ERC20_max_val_sent has 16 (16.0%) missing valuesMissing
ERC20_avg_val_sent has 16 (16.0%) missing valuesMissing
ERC20_min_val_sent_contract has 16 (16.0%) missing valuesMissing
ERC20_max_val_sent_contract has 16 (16.0%) missing valuesMissing
ERC20_avg_val_sent_contract has 16 (16.0%) missing valuesMissing
ERC20_uniq_sent_token_name has 16 (16.0%) missing valuesMissing
ERC20_uniq_rec_token_name has 16 (16.0%) missing valuesMissing
Avg_min_between_sent_tnx has 48 (48.0%) zerosZeros
Avg_min_between_received_tnx has 36 (36.0%) zerosZeros
Time_Diff_between_first_and_last_(Mins) has 10 (10.0%) zerosZeros
min_value_received has 27 (27.0%) zerosZeros
max_value_received has 11 (11.0%) zerosZeros
avg_val_received has 11 (11.0%) zerosZeros
min_val_sent has 39 (39.0%) zerosZeros
max_val_sent has 28 (28.0%) zerosZeros
avg_val_sent has 28 (28.0%) zerosZeros
total_Ether_sent has 28 (28.0%) zerosZeros
total_ether_received has 11 (11.0%) zerosZeros
total_ether_balance has 11 (11.0%) zerosZeros
Total_ERC20_tnxs has 32 (32.0%) zerosZeros
ERC20_total_Ether_received has 34 (34.0%) zerosZeros
ERC20_total_ether_sent has 71 (71.0%) zerosZeros
ERC20_uniq_sent_addr has 71 (71.0%) zerosZeros
ERC20_uniq_rec_addr has 34 (34.0%) zerosZeros
ERC20_uniq_rec_contract_addr has 34 (34.0%) zerosZeros
ERC20_min_val_rec has 54 (54.0%) zerosZeros
ERC20_max_val_rec has 35 (35.0%) zerosZeros
ERC20_avg_val_rec has 35 (35.0%) zerosZeros
ERC20_min_val_sent has 76 (76.0%) zerosZeros
ERC20_max_val_sent has 71 (71.0%) zerosZeros
ERC20_avg_val_sent has 71 (71.0%) zerosZeros
ERC20_uniq_sent_token_name has 71 (71.0%) zerosZeros
ERC20_uniq_rec_token_name has 34 (34.0%) zerosZeros

Reproduction

Analysis started2023-04-10 22:09:45.551701
Analysis finished2023-04-10 22:12:25.218058
Duration2 minutes and 39.67 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Avg_min_between_sent_tnx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3478.1513
Minimum0
Maximum190984.02
Zeros48
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:25.438910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.19
Q3107.885
95-th percentile11974.428
Maximum190984.02
Range190984.02
Interquartile range (IQR)107.885

Descriptive statistics

Standard deviation19751.413
Coefficient of variation (CV)5.67871
Kurtosis84.253749
Mean3478.1513
Median Absolute Deviation (MAD)2.19
Skewness8.9120295
Sum347815.13
Variance3.901183 × 108
MonotonicityNot monotonic
2023-04-10T19:12:25.704489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
48.0%
21.65 1
 
1.0%
161.16 1
 
1.0%
23.82 1
 
1.0%
67.96 1
 
1.0%
18.96 1
 
1.0%
2.06 1
 
1.0%
209.73 1
 
1.0%
190984.02 1
 
1.0%
1525.53 1
 
1.0%
Other values (43) 43
43.0%
ValueCountFrequency (%)
0 48
48.0%
1.39 1
 
1.0%
2.06 1
 
1.0%
2.32 1
 
1.0%
2.61 1
 
1.0%
2.66 1
 
1.0%
3.19 1
 
1.0%
6.27 1
 
1.0%
6.4 1
 
1.0%
7.78 1
 
1.0%
ValueCountFrequency (%)
190984.02 1
1.0%
42309.29 1
1.0%
23534.96 1
1.0%
19045.57 1
1.0%
16601.46 1
1.0%
11730.9 1
1.0%
8364.27 1
1.0%
8123.18 1
1.0%
7133.41 1
1.0%
3841.08 1
1.0%

Avg_min_between_received_tnx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2945.5134
Minimum0
Maximum43069.21
Zeros36
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:26.001333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median76.63
Q32377.625
95-th percentile18895.342
Maximum43069.21
Range43069.21
Interquartile range (IQR)2377.625

Descriptive statistics

Standard deviation7262.3366
Coefficient of variation (CV)2.4655589
Kurtosis14.462343
Mean2945.5134
Median Absolute Deviation (MAD)76.63
Skewness3.6623714
Sum294551.34
Variance52741534
MonotonicityNot monotonic
2023-04-10T19:12:26.273911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
36.0%
0.82 2
 
2.0%
1436.76 1
 
1.0%
1880.12 1
 
1.0%
1411.11 1
 
1.0%
774.99 1
 
1.0%
43069.21 1
 
1.0%
34602.99 1
 
1.0%
1905.25 1
 
1.0%
145.25 1
 
1.0%
Other values (54) 54
54.0%
ValueCountFrequency (%)
0 36
36.0%
0.02 1
 
1.0%
0.03 1
 
1.0%
0.07 1
 
1.0%
0.14 1
 
1.0%
0.29 1
 
1.0%
0.47 1
 
1.0%
0.82 2
 
2.0%
4.72 1
 
1.0%
8.97 1
 
1.0%
ValueCountFrequency (%)
43069.21 1
1.0%
34602.99 1
1.0%
31214.68 1
1.0%
22249.36 1
1.0%
20159.64 1
1.0%
18828.8 1
1.0%
12101.42 1
1.0%
11703.84 1
1.0%
11682.08 1
1.0%
8132.58 1
1.0%

Time_Diff_between_first_and_last_(Mins)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135117.94
Minimum0
Maximum1098915.1
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:26.560408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q175.155
median8557.035
Q3111015
95-th percentile741385.35
Maximum1098915.1
Range1098915.1
Interquartile range (IQR)110939.85

Descriptive statistics

Standard deviation255039.08
Coefficient of variation (CV)1.8875294
Kurtosis4.6311476
Mean135117.94
Median Absolute Deviation (MAD)8557.035
Skewness2.2904477
Sum13511794
Variance6.5044933 × 1010
MonotonicityNot monotonic
2023-04-10T19:12:26.826004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
10.0%
511175.65 1
 
1.0%
105168.15 1
 
1.0%
7.98 1
 
1.0%
32035.63 1
 
1.0%
956179.85 1
 
1.0%
9.42 1
 
1.0%
561.93 1
 
1.0%
57879.03 1
 
1.0%
9813.18 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0 10
10.0%
4.18 1
 
1.0%
7.98 1
 
1.0%
9.42 1
 
1.0%
46.45 1
 
1.0%
56.58 1
 
1.0%
56.87 1
 
1.0%
59.38 1
 
1.0%
61.48 1
 
1.0%
64.37 1
 
1.0%
ValueCountFrequency (%)
1098915.1 1
1.0%
994962.07 1
1.0%
960129.8 1
1.0%
956179.85 1
1.0%
900382.7 1
1.0%
733017.07 1
1.0%
684318.4 1
1.0%
619360.05 1
1.0%
564638.3 1
1.0%
511175.65 1
1.0%

min_value_received
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.781391
Minimum0
Maximum2435.6107
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:27.107225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0486195
Q31.3352795
95-th percentile101
Maximum2435.6107
Range2435.6107
Interquartile range (IQR)1.3352795

Descriptive statistics

Standard deviation257.73234
Coefficient of variation (CV)5.2834152
Kurtosis76.203128
Mean48.781391
Median Absolute Deviation (MAD)0.0486195
Skewness8.3683049
Sum4878.1391
Variance66425.961
MonotonicityNot monotonic
2023-04-10T19:12:27.419697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
27.0%
101 7
 
7.0%
0.1 4
 
4.0%
0.01 3
 
3.0%
0.5 3
 
3.0%
0.001 2
 
2.0%
5 2
 
2.0%
1 2
 
2.0%
0.49 2
 
2.0%
0.769287 1
 
1.0%
Other values (47) 47
47.0%
ValueCountFrequency (%)
0 27
27.0%
0.0005 1
 
1.0%
0.000888 1
 
1.0%
0.000926 1
 
1.0%
0.001 2
 
2.0%
0.001054 1
 
1.0%
0.003 1
 
1.0%
0.005848 1
 
1.0%
0.008 1
 
1.0%
0.01 3
 
3.0%
ValueCountFrequency (%)
2435.610738 1
 
1.0%
525.016521 1
 
1.0%
517.428338 1
 
1.0%
512.388539 1
 
1.0%
101 7
7.0%
50.311864 1
 
1.0%
35.677299 1
 
1.0%
15.626123 1
 
1.0%
13 1
 
1.0%
11.861025 1
 
1.0%

max_value_received
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.65873
Minimum0
Maximum7000
Zeros11
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:27.955619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.65587975
median4.506846
Q331.362271
95-th percentile269.85329
Maximum7000
Range7000
Interquartile range (IQR)30.706392

Descriptive statistics

Standard deviation845.79461
Coefficient of variation (CV)4.6817256
Kurtosis48.314096
Mean180.65873
Median Absolute Deviation (MAD)4.488846
Skewness6.6738172
Sum18065.873
Variance715368.53
MonotonicityNot monotonic
2023-04-10T19:12:28.205589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
11.0%
101 7
 
7.0%
5 5
 
5.0%
10 4
 
4.0%
1 3
 
3.0%
0.1 2
 
2.0%
1.3 2
 
2.0%
50.449086 1
 
1.0%
1483.571662 1
 
1.0%
0.987 1
 
1.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
0 11
11.0%
0.011 1
 
1.0%
0.025 1
 
1.0%
0.081312 1
 
1.0%
0.1 2
 
2.0%
0.103176 1
 
1.0%
0.137001 1
 
1.0%
0.2671 1
 
1.0%
0.353037 1
 
1.0%
0.499344 1
 
1.0%
ValueCountFrequency (%)
7000 1
 
1.0%
4271.378867 1
 
1.0%
1488.611461 1
 
1.0%
1483.571662 1
 
1.0%
1475.983479 1
 
1.0%
206.372758 1
 
1.0%
187.06 1
 
1.0%
169.995 1
 
1.0%
101 7
7.0%
100 1
 
1.0%

avg_val_received
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.681088
Minimum0
Maximum3333.6667
Zeros11
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:28.486733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3271765
median1.2249035
Q310
95-th percentile144.85999
Maximum3333.6667
Range3333.6667
Interquartile range (IQR)9.6728235

Descriptive statistics

Standard deviation381.63451
Coefficient of variation (CV)4.4027425
Kurtosis54.523618
Mean86.681088
Median Absolute Deviation (MAD)1.2248985
Skewness6.8949963
Sum8668.1088
Variance145644.9
MonotonicityNot monotonic
2023-04-10T19:12:28.752646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
11.0%
101 7
 
7.0%
50.5 6
 
6.0%
1000.5 3
 
3.0%
1 2
 
2.0%
5 2
 
2.0%
0.1 2
 
2.0%
4.499779 1
 
1.0%
0.171657 1
 
1.0%
1.192081 1
 
1.0%
Other values (64) 64
64.0%
ValueCountFrequency (%)
0 11
11.0%
1 × 10-51
 
1.0%
0.025 1
 
1.0%
0.046829 1
 
1.0%
0.064736 1
 
1.0%
0.071885 1
 
1.0%
0.1 2
 
2.0%
0.109738 1
 
1.0%
0.120739 1
 
1.0%
0.156006 1
 
1.0%
ValueCountFrequency (%)
3333.666667 1
 
1.0%
1000.5 3
3.0%
978.199868 1
 
1.0%
101 7
7.0%
58.551009 1
 
1.0%
54.674138 1
 
1.0%
50.5 6
6.0%
33.828488 1
 
1.0%
33.666667 1
 
1.0%
26.688889 1
 
1.0%

min_val_sent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5496884
Minimum0
Maximum49.137992
Zeros39
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:29.033229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0985145
Q31.356543
95-th percentile11.489968
Maximum49.137992
Range49.137992
Interquartile range (IQR)1.356543

Descriptive statistics

Standard deviation6.5242888
Coefficient of variation (CV)2.5588573
Kurtosis27.851094
Mean2.5496884
Median Absolute Deviation (MAD)0.0985145
Skewness4.714759
Sum254.96884
Variance42.566344
MonotonicityNot monotonic
2023-04-10T19:12:29.328859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39
39.0%
1 5
 
5.0%
0.5 3
 
3.0%
4.29895 1
 
1.0%
0.199072 1
 
1.0%
3.99911 1
 
1.0%
0.130707 1
 
1.0%
5.959353 1
 
1.0%
0.004 1
 
1.0%
4.99938 1
 
1.0%
Other values (46) 46
46.0%
ValueCountFrequency (%)
0 39
39.0%
0.000198 1
 
1.0%
0.001078 1
 
1.0%
0.004 1
 
1.0%
0.01 1
 
1.0%
0.023606 1
 
1.0%
0.041896 1
 
1.0%
0.049675 1
 
1.0%
0.06709 1
 
1.0%
0.09688 1
 
1.0%
ValueCountFrequency (%)
49.137992 1
1.0%
25.587227 1
1.0%
21.057 1
1.0%
19.517005 1
1.0%
12.99 1
1.0%
11.411019 1
1.0%
10.483054 1
1.0%
9.999496 1
1.0%
8.973337 1
1.0%
7.813146 1
1.0%

max_val_sent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.40909
Minimum0
Maximum28903.575
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:29.658123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q321.610579
95-th percentile193.49035
Maximum28903.575
Range28903.575
Interquartile range (IQR)21.610579

Descriptive statistics

Standard deviation2895.0333
Coefficient of variation (CV)8.1917342
Kurtosis98.417794
Mean353.40909
Median Absolute Deviation (MAD)2
Skewness9.8858825
Sum35340.909
Variance8381217.9
MonotonicityNot monotonic
2023-04-10T19:12:29.957947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
28.0%
2 2
 
2.0%
91.187396 1
 
1.0%
28.228 1
 
1.0%
1452.455404 1
 
1.0%
4.99938 1
 
1.0%
0.573212 1
 
1.0%
5.959353 1
 
1.0%
3.441286 1
 
1.0%
4.22 1
 
1.0%
Other values (62) 62
62.0%
ValueCountFrequency (%)
0 28
28.0%
0.039509 1
 
1.0%
0.089 1
 
1.0%
0.09688 1
 
1.0%
0.136312 1
 
1.0%
0.202359 1
 
1.0%
0.266639 1
 
1.0%
0.352617 1
 
1.0%
0.5 1
 
1.0%
0.507617 1
 
1.0%
ValueCountFrequency (%)
28903.57479 1
1.0%
1519.64391 1
1.0%
1470.620309 1
1.0%
1452.455404 1
1.0%
312 1
1.0%
187.253 1
1.0%
101 1
1.0%
99.454491 1
1.0%
98.998428 1
1.0%
93.65414 1
1.0%

avg_val_sent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.448687
Minimum0
Maximum2474.2691
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:30.239609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2553355
Q311.26079
95-th percentile65.019767
Maximum2474.2691
Range2474.2691
Interquartile range (IQR)11.26079

Descriptive statistics

Standard deviation266.47503
Coefficient of variation (CV)5.179433
Kurtosis70.946507
Mean51.448687
Median Absolute Deviation (MAD)1.2553355
Skewness8.0450911
Sum5144.8687
Variance71008.939
MonotonicityNot monotonic
2023-04-10T19:12:30.519172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
28.0%
33.666149 7
 
7.0%
666.999528 2
 
2.0%
33.666143 2
 
2.0%
4.99938 1
 
1.0%
0.170796 1
 
1.0%
5.959353 1
 
1.0%
0.648474 1
 
1.0%
1.065353 1
 
1.0%
3.244 1
 
1.0%
Other values (55) 55
55.0%
ValueCountFrequency (%)
0 28
28.0%
2.9 × 10-51
 
1.0%
0.03285 1
 
1.0%
0.075958 1
 
1.0%
0.09688 1
 
1.0%
0.109202 1
 
1.0%
0.13332 1
 
1.0%
0.170796 1
 
1.0%
0.235476 1
 
1.0%
0.322818 1
 
1.0%
ValueCountFrequency (%)
2474.269105 1
 
1.0%
666.999528 2
 
2.0%
500.249528 1
 
1.0%
99.019293 1
 
1.0%
63.230318 1
 
1.0%
50.499487 1
 
1.0%
33.666163 1
 
1.0%
33.666155 1
 
1.0%
33.666149 7
7.0%
33.666146 1
 
1.0%

total_Ether_sent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean644.03906
Minimum0
Maximum42062.575
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:30.800318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.2447585
Q367.927509
95-th percentile1449.4441
Maximum42062.575
Range42062.575
Interquartile range (IQR)67.927509

Descriptive statistics

Standard deviation4316.4464
Coefficient of variation (CV)6.7021501
Kurtosis88.127982
Mean644.03906
Median Absolute Deviation (MAD)3.2447585
Skewness9.209567
Sum64403.906
Variance18631710
MonotonicityNot monotonic
2023-04-10T19:12:31.065919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
28.0%
100.998446 7
 
7.0%
2000.998585 2
 
2.0%
100.9984278 2
 
2.0%
4.99938 1
 
1.0%
1.195573 1
 
1.0%
5.95935347 1
 
1.0%
4.53932036 1
 
1.0%
2.13070654 1
 
1.0%
16.22 1
 
1.0%
Other values (55) 55
55.0%
ValueCountFrequency (%)
0 28
28.0%
0.0395093 1
 
1.0%
0.09688 1
 
1.0%
0.098551 1
 
1.0%
0.26663926 1
 
1.0%
0.35261718 1
 
1.0%
0.5 1
 
1.0%
0.594496 1
 
1.0%
0.639139 1
 
1.0%
0.75612671 1
 
1.0%
ValueCountFrequency (%)
42062.57479 1
1.0%
10000.94855 1
1.0%
2000.998585 2
2.0%
2000.998113 1
1.0%
1420.414967 1
1.0%
1097.941133 1
1.0%
1011.685094 1
1.0%
393.2782717 1
1.0%
313.9 1
1.0%
270.853 1
1.0%

total_ether_received
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean824.42576
Minimum0
Maximum42062.594
Zeros11
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:31.349831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2827094
median13.08568
Q3101
95-th percentile2001
Maximum42062.594
Range42062.594
Interquartile range (IQR)99.717291

Descriptive statistics

Standard deviation4439.5174
Coefficient of variation (CV)5.3849815
Kurtosis77.226675
Mean824.42576
Median Absolute Deviation (MAD)13.08568
Skewness8.4548041
Sum82442.576
Variance19709315
MonotonicityNot monotonic
2023-04-10T19:12:31.599896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 14
 
14.0%
0 11
 
11.0%
2001 3
 
3.0%
5 2
 
2.0%
0.1 2
 
2.0%
1 2
 
2.0%
7.407787617 1
 
1.0%
2.939 1
 
1.0%
393.3992527 1
 
1.0%
21.05979583 1
 
1.0%
Other values (62) 62
62.0%
ValueCountFrequency (%)
0 11
11.0%
0.022 1
 
1.0%
0.0936584 1
 
1.0%
0.1 2
 
2.0%
0.2671 1
 
1.0%
0.3530372 1
 
1.0%
0.595 1
 
1.0%
0.64 1
 
1.0%
0.75663071 1
 
1.0%
0.98029 1
 
1.0%
ValueCountFrequency (%)
42062.59433 1
 
1.0%
10187.8755 1
 
1.0%
10001 1
 
1.0%
5303.391356 1
 
1.0%
2001 3
3.0%
1420.872369 1
 
1.0%
1080.700411 1
 
1.0%
1011.697327 1
 
1.0%
764.1994771 1
 
1.0%
611.3820377 1
 
1.0%

total_ether_balance
Real number (ℝ)

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.3867
Minimum-32.099159
Maximum10187.876
Zeros11
Zeros (%)11.0%
Negative8
Negative (%)8.0%
Memory size1.6 KiB
2023-04-10T19:12:31.901692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-32.099159
5-th percentile-0.50969955
Q10.000591
median0.001554
Q30.048335522
95-th percentile252.35163
Maximum10187.876
Range10219.975
Interquartile range (IQR)0.047744522

Descriptive statistics

Standard deviation1145.7318
Coefficient of variation (CV)6.3515312
Kurtosis63.666373
Mean180.3867
Median Absolute Deviation (MAD)0.001554
Skewness7.7884801
Sum18038.67
Variance1312701.3
MonotonicityNot monotonic
2023-04-10T19:12:32.167611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
11.0%
0.001554 7
 
7.0%
0.000861 6
 
6.0%
0.00105 4
 
4.0%
0.000504 2
 
2.0%
0.001414909 2
 
2.0%
0.001572218 2
 
2.0%
0.006027 1
 
1.0%
0.047297362 1
 
1.0%
8.12809039 1
 
1.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
-32.09915869 1
 
1.0%
-22.89 1
 
1.0%
-17.24072198 1
 
1.0%
-1.48971 1
 
1.0%
-0.70316795 1
 
1.0%
-0.499517 1
 
1.0%
-0.48777454 1
 
1.0%
-0.2251 1
 
1.0%
0 11
11.0%
0.00042002 1
 
1.0%
ValueCountFrequency (%)
10187.8755 1
1.0%
5303.391356 1
1.0%
764.1994771 1
1.0%
611.3820377 1
1.0%
483.2325508 1
1.0%
240.2 1
1.0%
160.2071435 1
1.0%
66.53886848 1
1.0%
58.10139867 1
1.0%
49.99894413 1
1.0%

Total_ERC20_tnxs
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)20.2%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean10.809524
Minimum0
Maximum498
Zeros32
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:32.386411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile33.05
Maximum498
Range498
Interquartile range (IQR)3

Descriptive statistics

Standard deviation55.677963
Coefficient of variation (CV)5.1508248
Kurtosis72.906986
Mean10.809524
Median Absolute Deviation (MAD)1
Skewness8.3292778
Sum908
Variance3100.0356
MonotonicityNot monotonic
2023-04-10T19:12:32.605147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 32
32.0%
1 22
22.0%
3 6
 
6.0%
2 4
 
4.0%
7 3
 
3.0%
4 3
 
3.0%
6 3
 
3.0%
5 2
 
2.0%
22 1
 
1.0%
75 1
 
1.0%
Other values (7) 7
 
7.0%
(Missing) 16
16.0%
ValueCountFrequency (%)
0 32
32.0%
1 22
22.0%
2 4
 
4.0%
3 6
 
6.0%
4 3
 
3.0%
5 2
 
2.0%
6 3
 
3.0%
7 3
 
3.0%
8 1
 
1.0%
10 1
 
1.0%
ValueCountFrequency (%)
498 1
 
1.0%
95 1
 
1.0%
75 1
 
1.0%
45 1
 
1.0%
35 1
 
1.0%
22 1
 
1.0%
11 1
 
1.0%
10 1
 
1.0%
8 1
 
1.0%
7 3
3.0%

ERC20_total_Ether_received
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)39.3%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean134457.56
Minimum0
Maximum8224365.2
Zeros34
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:32.839488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.337
Q352.543302
95-th percentile371109.53
Maximum8224365.2
Range8224365.2
Interquartile range (IQR)52.543302

Descriptive statistics

Standard deviation907449.43
Coefficient of variation (CV)6.7489656
Kurtosis78.720761
Mean134457.56
Median Absolute Deviation (MAD)1.337
Skewness8.7625836
Sum11294435
Variance8.2346447 × 1011
MonotonicityNot monotonic
2023-04-10T19:12:33.074388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 34
34.0%
1.337 14
14.0%
13.37 5
 
5.0%
100.337 2
 
2.0%
1.603598952 1
 
1.0%
664.8515447 1
 
1.0%
19.61887385 1
 
1.0%
917.1005762 1
 
1.0%
397236.8967 1
 
1.0%
464323.1929 1
 
1.0%
Other values (23) 23
23.0%
(Missing) 16
16.0%
ValueCountFrequency (%)
0 34
34.0%
1 × 10-121
 
1.0%
0.889002255 1
 
1.0%
1.179313258 1
 
1.0%
1.337 14
14.0%
1.603598952 1
 
1.0%
1.816217033 1
 
1.0%
2.097621132 1
 
1.0%
2.329898685 1
 
1.0%
4.211841121 1
 
1.0%
ValueCountFrequency (%)
8224365.159 1
1.0%
1141890.121 1
1.0%
701817.7156 1
1.0%
464323.1929 1
1.0%
397236.8967 1
1.0%
223054.4271 1
1.0%
59980.25284 1
1.0%
49768.85779 1
1.0%
15105.337 1
1.0%
7812 1
1.0%

ERC20_total_ether_sent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)15.5%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean96401.911
Minimum0
Maximum6068738.9
Zeros71
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:33.283060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile56529.774
Maximum6068738.9
Range6068738.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation673441.72
Coefficient of variation (CV)6.9857715
Kurtosis77.077033
Mean96401.911
Median Absolute Deviation (MAD)0
Skewness8.6535252
Sum8097760.5
Variance4.5352375 × 1011
MonotonicityNot monotonic
2023-04-10T19:12:33.472876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 71
71.0%
100 2
 
2.0%
1141889.49 1
 
1.0%
7812 1
 
1.0%
36.61203433 1
 
1.0%
18797.00042 1
 
1.0%
1216.789506 1
 
1.0%
6068738.885 1
 
1.0%
20.83224224 1
 
1.0%
49666.85779 1
 
1.0%
Other values (3) 3
 
3.0%
(Missing) 16
 
16.0%
ValueCountFrequency (%)
0 71
71.0%
20.83224224 1
 
1.0%
36.61203433 1
 
1.0%
100 2
 
2.0%
1216.789506 1
 
1.0%
7812 1
 
1.0%
18797.00042 1
 
1.0%
49666.85779 1
 
1.0%
57740.8763 1
 
1.0%
354504.2989 1
 
1.0%
ValueCountFrequency (%)
6068738.885 1
1.0%
1141889.49 1
1.0%
397136.8967 1
1.0%
354504.2989 1
1.0%
57740.8763 1
1.0%
49666.85779 1
1.0%
18797.00042 1
1.0%
7812 1
1.0%
1216.789506 1
1.0%
100 2
2.0%

ERC20_total_Ether_sent_contract
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)2.4%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
83 
5.57e-05
 
1

Length

Max length8
Median length3
Mean length3.0595238
Min length3

Characters and Unicode

Total characters257
Distinct characters6
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 83
83.0%
5.57e-05 1
 
1.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:33.691782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:33.926490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 83
98.8%
5.57e-05 1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 167
65.0%
. 84
32.7%
5 3
 
1.2%
7 1
 
0.4%
e 1
 
0.4%
- 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 171
66.5%
Other Punctuation 84
32.7%
Lowercase Letter 1
 
0.4%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 167
97.7%
5 3
 
1.8%
7 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 256
99.6%
Latin 1
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 167
65.2%
. 84
32.8%
5 3
 
1.2%
7 1
 
0.4%
- 1
 
0.4%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 167
65.0%
. 84
32.7%
5 3
 
1.2%
7 1
 
0.4%
e 1
 
0.4%
- 1
 
0.4%

ERC20_uniq_sent_addr
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)8.3%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean0.70238095
Minimum0
Maximum21
Zeros71
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:34.098347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.85
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9484722
Coefficient of variation (CV)4.1978248
Kurtosis33.545632
Mean0.70238095
Median Absolute Deviation (MAD)0
Skewness5.6149942
Sum59
Variance8.6934882
MonotonicityNot monotonic
2023-04-10T19:12:34.520420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 71
71.0%
1 8
 
8.0%
7 1
 
1.0%
6 1
 
1.0%
15 1
 
1.0%
21 1
 
1.0%
2 1
 
1.0%
(Missing) 16
 
16.0%
ValueCountFrequency (%)
0 71
71.0%
1 8
 
8.0%
2 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
15 1
 
1.0%
21 1
 
1.0%
ValueCountFrequency (%)
21 1
 
1.0%
15 1
 
1.0%
7 1
 
1.0%
6 1
 
1.0%
2 1
 
1.0%
1 8
 
8.0%
0 71
71.0%

ERC20_uniq_rec_addr
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)16.7%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean4.7261905
Minimum0
Maximum153
Zeros34
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:34.696320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile21.6
Maximum153
Range153
Interquartile range (IQR)3

Descriptive statistics

Standard deviation17.684212
Coefficient of variation (CV)3.7417476
Kurtosis61.203677
Mean4.7261905
Median Absolute Deviation (MAD)1
Skewness7.4410529
Sum397
Variance312.73135
MonotonicityNot monotonic
2023-04-10T19:12:34.901539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 34
34.0%
1 21
21.0%
2 6
 
6.0%
3 6
 
6.0%
6 3
 
3.0%
4 3
 
3.0%
7 2
 
2.0%
8 2
 
2.0%
5 2
 
2.0%
38 1
 
1.0%
Other values (4) 4
 
4.0%
(Missing) 16
16.0%
ValueCountFrequency (%)
0 34
34.0%
1 21
21.0%
2 6
 
6.0%
3 6
 
6.0%
4 3
 
3.0%
5 2
 
2.0%
6 3
 
3.0%
7 2
 
2.0%
8 2
 
2.0%
24 1
 
1.0%
ValueCountFrequency (%)
153 1
 
1.0%
38 1
 
1.0%
33 1
 
1.0%
28 1
 
1.0%
24 1
 
1.0%
8 2
2.0%
7 2
2.0%
6 3
3.0%
5 2
2.0%
4 3
3.0%

ERC20_uniq_sent_addr.1
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)2.4%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
83 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 83
83.0%
1.0 1
 
1.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:35.121163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:35.358208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 83
98.8%
1.0 1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 167
66.3%
. 84
33.3%
1 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 167
99.4%
1 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 167
66.3%
. 84
33.3%
1 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 167
66.3%
. 84
33.3%
1 1
 
0.4%

ERC20_uniq_rec_contract_addr
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)17.9%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean5.7857143
Minimum0
Maximum213
Zeros34
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:35.530147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile27.15
Maximum213
Range213
Interquartile range (IQR)3

Descriptive statistics

Standard deviation24.224389
Coefficient of variation (CV)4.1869313
Kurtosis66.426214
Mean5.7857143
Median Absolute Deviation (MAD)1
Skewness7.8374297
Sum486
Variance586.821
MonotonicityNot monotonic
2023-04-10T19:12:35.719563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 34
34.0%
1 21
21.0%
3 6
 
6.0%
2 5
 
5.0%
4 4
 
4.0%
6 3
 
3.0%
7 2
 
2.0%
5 2
 
2.0%
50 1
 
1.0%
30 1
 
1.0%
Other values (5) 5
 
5.0%
(Missing) 16
16.0%
ValueCountFrequency (%)
0 34
34.0%
1 21
21.0%
2 5
 
5.0%
3 6
 
6.0%
4 4
 
4.0%
5 2
 
2.0%
6 3
 
3.0%
7 2
 
2.0%
10 1
 
1.0%
11 1
 
1.0%
ValueCountFrequency (%)
213 1
 
1.0%
50 1
 
1.0%
33 1
 
1.0%
32 1
 
1.0%
30 1
 
1.0%
11 1
 
1.0%
10 1
 
1.0%
7 2
2.0%
6 3
3.0%
5 2
2.0%

ERC20_avg_time_between_sent_tnx
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.2%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
84 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84
84.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:35.924910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:36.159641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84
100.0%

Most occurring characters

ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
100.0%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

ERC20_avg_time_between_rec_tnx
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.2%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
84 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84
84.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:36.334586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:36.537762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84
100.0%

Most occurring characters

ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
100.0%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

ERC20_avg_time_between_rec_2_tnx
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.2%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
84 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84
84.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:36.709618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:36.931719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84
100.0%

Most occurring characters

ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
100.0%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

ERC20_avg_time_between_contract_tnx
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.2%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
84 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84
84.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:37.103494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:37.316269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84
100.0%

Most occurring characters

ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
100.0%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

ERC20_min_val_rec
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)16.7%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean94.140827
Minimum0
Maximum7812
Zeros54
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:37.503663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.08425
95-th percentile13.37
Maximum7812
Range7812
Interquartile range (IQR)1.08425

Descriptive statistics

Standard deviation852.23897
Coefficient of variation (CV)9.0528095
Kurtosis83.997602
Mean94.140827
Median Absolute Deviation (MAD)0
Skewness9.1649577
Sum7907.8295
Variance726311.27
MonotonicityNot monotonic
2023-04-10T19:12:37.706846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 54
54.0%
1.337 12
 
12.0%
13.37 5
 
5.0%
1 3
 
3.0%
7812 1
 
1.0%
0.423705 1
 
1.0%
2.097621 1
 
1.0%
0.541648 1
 
1.0%
0.760186 1
 
1.0%
0.317841 1
 
1.0%
Other values (4) 4
 
4.0%
(Missing) 16
 
16.0%
ValueCountFrequency (%)
0 54
54.0%
1 × 10-61
 
1.0%
0.317841 1
 
1.0%
0.423705 1
 
1.0%
0.424541 1
 
1.0%
0.541648 1
 
1.0%
0.760186 1
 
1.0%
1 3
 
3.0%
1.337 12
 
12.0%
1.603599 1
 
1.0%
ValueCountFrequency (%)
7812 1
 
1.0%
13.37 5
5.0%
3.766315 1
 
1.0%
2.097621 1
 
1.0%
1.603599 1
 
1.0%
1.337 12
12.0%
1 3
 
3.0%
0.760186 1
 
1.0%
0.541648 1
 
1.0%
0.424541 1
 
1.0%

ERC20_max_val_rec
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)35.7%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean28744.203
Minimum0
Maximum950000
Zeros35
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:37.975863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.337
Q338.479526
95-th percentile97221.325
Maximum950000
Range950000
Interquartile range (IQR)38.479526

Descriptive statistics

Standard deviation127978.67
Coefficient of variation (CV)4.4523298
Kurtosis35.674089
Mean28744.203
Median Absolute Deviation (MAD)1.337
Skewness5.6944255
Sum2414513.1
Variance1.6378541 × 1010
MonotonicityNot monotonic
2023-04-10T19:12:38.208411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 35
35.0%
1.337 14
 
14.0%
13.37 5
 
5.0%
600 3
 
3.0%
99 2
 
2.0%
1 1
 
1.0%
513.387025 1
 
1.0%
0.888889 1
 
1.0%
50000 1
 
1.0%
2.894 1
 
1.0%
Other values (20) 20
20.0%
(Missing) 16
16.0%
ValueCountFrequency (%)
0 35
35.0%
0.755608 1
 
1.0%
0.888889 1
 
1.0%
1 1
 
1.0%
1.056031 1
 
1.0%
1.337 14
 
14.0%
1.603599 1
 
1.0%
2.097621 1
 
1.0%
2.894 1
 
1.0%
13.37 5
 
5.0%
ValueCountFrequency (%)
950000 1
1.0%
534600 1
1.0%
342426.6 1
1.0%
325756.271 1
1.0%
105554.5 1
1.0%
50000 1
1.0%
49666.85779 1
1.0%
27403.57679 1
1.0%
15000 1
1.0%
7812 1
1.0%

ERC20_avg_val_rec
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct35
Distinct (%)41.7%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean3212.6468
Minimum0
Maximum99309.224
Zeros35
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:38.427131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.337
Q315.04435
95-th percentile10628.626
Maximum99309.224
Range99309.224
Interquartile range (IQR)15.04435

Descriptive statistics

Standard deviation15126.124
Coefficient of variation (CV)4.708306
Kurtosis35.693595
Mean3212.6468
Median Absolute Deviation (MAD)1.337
Skewness5.9528371
Sum269862.33
Variance2.2879963 × 108
MonotonicityNot monotonic
2023-04-10T19:12:38.679973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 35
35.0%
1.337 12
 
12.0%
13.37 5
 
5.0%
0.445667 1
 
1.0%
0.776633 1
 
1.0%
72.409592 1
 
1.0%
158.073499 1
 
1.0%
301.883157 1
 
1.0%
16589.61927 1
 
1.0%
5719.344284 1
 
1.0%
Other values (25) 25
25.0%
(Missing) 16
16.0%
ValueCountFrequency (%)
0 35
35.0%
0.1778 1
 
1.0%
0.445667 1
 
1.0%
0.589657 1
 
1.0%
0.6685 1
 
1.0%
0.776633 1
 
1.0%
0.908109 1
 
1.0%
1.337 12
 
12.0%
1.403947 1
 
1.0%
1.603599 1
 
1.0%
ValueCountFrequency (%)
99309.22418 1
1.0%
95157.5101 1
1.0%
18734.31699 1
1.0%
16589.61927 1
1.0%
10797.19562 1
1.0%
9673.399851 1
1.0%
7812 1
1.0%
5719.344284 1
1.0%
2517.556167 1
1.0%
1764.125083 1
1.0%

ERC20_min_val_sent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)9.5%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean653.77126
Minimum0
Maximum49666.858
Zeros76
Zeros (%)76.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:38.883455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile100
Maximum49666.858
Range49666.858
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5423.401
Coefficient of variation (CV)8.2955635
Kurtosis83.282783
Mean653.77126
Median Absolute Deviation (MAD)0
Skewness9.1088739
Sum54916.786
Variance29413278
MonotonicityNot monotonic
2023-04-10T19:12:39.105577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 76
76.0%
100 2
 
2.0%
2812 1
 
1.0%
0.306034 1
 
1.0%
1216.789506 1
 
1.0%
20.832242 1
 
1.0%
49666.85779 1
 
1.0%
1000 1
 
1.0%
(Missing) 16
 
16.0%
ValueCountFrequency (%)
0 76
76.0%
0.306034 1
 
1.0%
20.832242 1
 
1.0%
100 2
 
2.0%
1000 1
 
1.0%
1216.789506 1
 
1.0%
2812 1
 
1.0%
49666.85779 1
 
1.0%
ValueCountFrequency (%)
49666.85779 1
 
1.0%
2812 1
 
1.0%
1216.789506 1
 
1.0%
1000 1
 
1.0%
100 2
 
2.0%
20.832242 1
 
1.0%
0.306034 1
 
1.0%
0 76
76.0%

ERC20_max_val_sent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)15.5%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean26410.358
Minimum0
Maximum1350000
Zeros71
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:39.313675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile48938.625
Maximum1350000
Range1350000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation155202.68
Coefficient of variation (CV)5.8765839
Kurtosis65.798846
Mean26410.358
Median Absolute Deviation (MAD)0
Skewness7.8439158
Sum2218470
Variance2.4087873 × 1010
MonotonicityNot monotonic
2023-04-10T19:12:39.554091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 71
71.0%
100 2
 
2.0%
342426 1
 
1.0%
5000 1
 
1.0%
18.306 1
 
1.0%
13799 1
 
1.0%
1216.789506 1
 
1.0%
1350000 1
 
1.0%
20.832242 1
 
1.0%
49666.85779 1
 
1.0%
Other values (3) 3
 
3.0%
(Missing) 16
 
16.0%
ValueCountFrequency (%)
0 71
71.0%
18.306 1
 
1.0%
20.832242 1
 
1.0%
100 2
 
2.0%
1216.789506 1
 
1.0%
5000 1
 
1.0%
13799 1
 
1.0%
44811.975 1
 
1.0%
49666.85779 1
 
1.0%
85554 1
 
1.0%
ValueCountFrequency (%)
1350000 1
1.0%
342426 1
1.0%
325756.271 1
1.0%
85554 1
1.0%
49666.85779 1
1.0%
44811.975 1
1.0%
13799 1
1.0%
5000 1
1.0%
1216.789506 1
1.0%
100 2
2.0%

ERC20_avg_val_sent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)15.5%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean5041.6238
Minimum0
Maximum132378.97
Zeros71
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:39.756347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9311.3542
Maximum132378.97
Range132378.97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22399.7
Coefficient of variation (CV)4.4429534
Kurtosis22.632698
Mean5041.6238
Median Absolute Deviation (MAD)0
Skewness4.8102175
Sum423496.4
Variance5.0174655 × 108
MonotonicityNot monotonic
2023-04-10T19:12:40.014686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 71
71.0%
100 2
 
2.0%
114188.949 1
 
1.0%
3906 1
 
1.0%
12.204011 1
 
1.0%
1879.700042 1
 
1.0%
1216.789506 1
 
1.0%
102859.9811 1
 
1.0%
20.832242 1
 
1.0%
49666.85779 1
 
1.0%
Other values (3) 3
 
3.0%
(Missing) 16
 
16.0%
ValueCountFrequency (%)
0 71
71.0%
12.204011 1
 
1.0%
20.832242 1
 
1.0%
100 2
 
2.0%
1216.789506 1
 
1.0%
1879.700042 1
 
1.0%
3906 1
 
1.0%
7542.644658 1
 
1.0%
9623.479383 1
 
1.0%
49666.85779 1
 
1.0%
ValueCountFrequency (%)
132378.9656 1
1.0%
114188.949 1
1.0%
102859.9811 1
1.0%
49666.85779 1
1.0%
9623.479383 1
1.0%
7542.644658 1
1.0%
3906 1
1.0%
1879.700042 1
1.0%
1216.789506 1
1.0%
100 2
2.0%

ERC20_min_val_sent_contract
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.2%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
84 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84
84.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:40.218183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:40.440676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84
100.0%

Most occurring characters

ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
100.0%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

ERC20_max_val_sent_contract
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.2%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
84 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84
84.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:40.612614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:40.832832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84
100.0%

Most occurring characters

ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
100.0%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

ERC20_avg_val_sent_contract
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.2%
Missing16
Missing (%)16.0%
Memory size1.6 KiB
0.0
84 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84
84.0%
(Missing) 16
 
16.0%

Length

2023-04-10T19:12:41.027731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T19:12:41.249221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84
100.0%

Most occurring characters

ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
100.0%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
66.7%
. 84
33.3%

ERC20_uniq_sent_token_name
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)9.5%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean1.202381
Minimum0
Maximum56
Zeros71
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:41.398162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.85
Maximum56
Range56
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.567132
Coefficient of variation (CV)5.4617732
Kurtosis60.983786
Mean1.202381
Median Absolute Deviation (MAD)0
Skewness7.5645361
Sum101
Variance43.127223
MonotonicityNot monotonic
2023-04-10T19:12:41.570677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 71
71.0%
1 7
 
7.0%
5 1
 
1.0%
6 1
 
1.0%
56 1
 
1.0%
3 1
 
1.0%
22 1
 
1.0%
2 1
 
1.0%
(Missing) 16
 
16.0%
ValueCountFrequency (%)
0 71
71.0%
1 7
 
7.0%
2 1
 
1.0%
3 1
 
1.0%
5 1
 
1.0%
6 1
 
1.0%
22 1
 
1.0%
56 1
 
1.0%
ValueCountFrequency (%)
56 1
 
1.0%
22 1
 
1.0%
6 1
 
1.0%
5 1
 
1.0%
3 1
 
1.0%
2 1
 
1.0%
1 7
 
7.0%
0 71
71.0%

ERC20_uniq_rec_token_name
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)17.9%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean5.7261905
Minimum0
Maximum211
Zeros34
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-04-10T19:12:41.758233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile27.15
Maximum211
Range211
Interquartile range (IQR)3

Descriptive statistics

Standard deviation23.993147
Coefficient of variation (CV)4.1900714
Kurtosis66.480215
Mean5.7261905
Median Absolute Deviation (MAD)1
Skewness7.8421604
Sum481
Variance575.67111
MonotonicityNot monotonic
2023-04-10T19:12:41.962077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 34
34.0%
1 21
21.0%
3 7
 
7.0%
2 5
 
5.0%
6 3
 
3.0%
4 3
 
3.0%
7 2
 
2.0%
5 2
 
2.0%
50 1
 
1.0%
30 1
 
1.0%
Other values (5) 5
 
5.0%
(Missing) 16
16.0%
ValueCountFrequency (%)
0 34
34.0%
1 21
21.0%
2 5
 
5.0%
3 7
 
7.0%
4 3
 
3.0%
5 2
 
2.0%
6 3
 
3.0%
7 2
 
2.0%
10 1
 
1.0%
11 1
 
1.0%
ValueCountFrequency (%)
211 1
 
1.0%
50 1
 
1.0%
32 1
 
1.0%
31 1
 
1.0%
30 1
 
1.0%
11 1
 
1.0%
10 1
 
1.0%
7 2
2.0%
6 3
3.0%
5 2
2.0%

Interactions

2023-04-10T19:12:15.311695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:47.444065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:53.802915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:59.669063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:05.709046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:11.303631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:17.451801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:23.351490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:29.132497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:34.872926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:40.702072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:46.192198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:51.926112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:57.652249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:03.618069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:09.122283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:14.843495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:20.527545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:26.484549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:32.269915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:38.232940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:44.026325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:49.841280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:55.735540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:02.040293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:08.956241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:15.582811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:47.692641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:54.046860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:59.921690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:05.941282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:11.567422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:17.702459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:23.575362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:29.380075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:35.121132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:40.967756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:46.436974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:52.170162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:57.909541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:03.859401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:09.364969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:15.066834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:20.784692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:26.704258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:32.538621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:38.468379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:44.284172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:50.084123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:56.002775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:02.376781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:09.246882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:15.802525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:47.916986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:54.234858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:00.140747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:06.172307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:11.775494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:17.921845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:23.768541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:29.593041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:35.310537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:41.148663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:46.619043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:52.368845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:58.117518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:04.054075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:09.591340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:15.534281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:20.998735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:26.917135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:32.758220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:38.675023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:44.476294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:50.283516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:56.201794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:02.650584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:09.460868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:16.019385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:48.166986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:54.440588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:00.395267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:06.400811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:12.010133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:18.151448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:23.975245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:29.817420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:35.522676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:41.381875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:46.839112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:52.579602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:58.380455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:04.264460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:09.818504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:15.768313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:21.239333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:27.117889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:32.992726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:38.891837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:44.725813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:50.524844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:56.428201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-10T19:10:33.350105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:39.141616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:44.731220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:50.482863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:56.097131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:01.884641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:07.658776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:13.292365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:19.049366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:24.901572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:30.808088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:36.590833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:42.584849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:48.335413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:54.251083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:00.102827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:07.180986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:13.629197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:20.251264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:52.322869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:58.134084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:04.367807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:10.016651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:16.042544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:21.974165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:27.788045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:33.551012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:39.392655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:44.941536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:50.667844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:56.295732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:02.093970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:07.860291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:13.525915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:19.277554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:25.134171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:31.018328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:36.808534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:42.773866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:48.532892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:54.442636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:00.353269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:07.448186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:13.867894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:20.482119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:52.543745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:58.352130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:04.580610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:10.219629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:16.271326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:22.207354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:28.002806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:33.758722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:39.617445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:45.150323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:50.862265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:56.517734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:02.547496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:08.068479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:13.734210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:19.437372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:25.350757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:31.234326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:37.059177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:42.974112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:48.757014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:54.656262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:00.619695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:07.713870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:14.109524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:20.673721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:52.808577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:58.569518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:04.800120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:10.457278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:16.490829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:22.441461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:28.207854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:33.970553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:39.826261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:45.355272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:51.075304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:56.735196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:02.750685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:08.272963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:13.941121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:19.632724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:25.578976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:31.434058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:37.289377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:43.182715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:48.968389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:54.859727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:00.910479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:07.981712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:14.339892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:20.917196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:53.084744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:58.792718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:05.020583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:10.684644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:16.745552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:22.684134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:28.464566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:34.199935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:40.059590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:45.592982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:51.309046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:56.958462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:02.983486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:08.502565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:14.176380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:19.919161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:25.814112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:31.650671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:37.531835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:43.416872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:49.200432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:55.109868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:01.210772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:08.252868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:14.631046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:21.139179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:53.342509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:59.008917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:05.258935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:10.905448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:17.008123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:22.903373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:28.718013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:34.451141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:40.303234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:45.791862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:51.519077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:57.182534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:03.190644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:08.728141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:14.403702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:20.151569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:26.050671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:31.872032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:37.789241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:43.634123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:49.417180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:55.328534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:01.506100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:08.468936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:14.883543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:21.351655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:53.586501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:09:59.218165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:05.501401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:11.109555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:17.227055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:23.134044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:28.934664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:34.670502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:40.509915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:45.985590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:51.734059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:10:57.401188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:03.401397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:08.932704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:14.634181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:20.350913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:26.267778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:32.084944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:37.999995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:43.834651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:49.629272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:11:55.545141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:01.786142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:08.709955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T19:12:15.105796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-10T19:12:42.227671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Avg_min_between_sent_tnxAvg_min_between_received_tnxTime_Diff_between_first_and_last_(Mins)min_value_receivedmax_value_receivedavg_val_receivedmin_val_sentmax_val_sentavg_val_senttotal_Ether_senttotal_ether_receivedtotal_ether_balanceTotal_ERC20_tnxsERC20_total_Ether_receivedERC20_total_ether_sentERC20_uniq_sent_addrERC20_uniq_rec_addrERC20_uniq_rec_contract_addrERC20_min_val_recERC20_max_val_recERC20_avg_val_recERC20_min_val_sentERC20_max_val_sentERC20_avg_val_sentERC20_uniq_sent_token_nameERC20_uniq_rec_token_nameERC20_total_Ether_sent_contractERC20_uniq_sent_addr.1
Avg_min_between_sent_tnx1.0000.2260.2240.2510.3050.2430.0620.5750.4720.6260.3050.0150.0970.1330.3770.3790.0920.096-0.3030.1390.1180.1670.3760.3740.3810.0980.0000.000
Avg_min_between_received_tnx0.2261.0000.779-0.4280.1330.019-0.336-0.070-0.1490.0130.3160.4800.4730.4160.2630.2650.4730.479-0.1100.3790.3650.0550.2610.2600.2690.4790.0000.000
Time_Diff_between_first_and_last_(Mins)0.2240.7791.000-0.3430.078-0.022-0.284-0.069-0.1430.0110.3010.4410.5770.4470.3120.3210.5740.572-0.0940.4070.3860.1050.3120.3070.3230.5690.4810.481
min_value_received0.251-0.428-0.3431.0000.4480.5940.7730.7290.7890.6850.243-0.196-0.607-0.543-0.189-0.188-0.596-0.595-0.343-0.519-0.517-0.135-0.188-0.188-0.187-0.5930.0000.000
max_value_received0.3050.1330.0780.4481.0000.9370.3010.6750.6570.6490.8850.259-0.118-0.1190.0570.068-0.099-0.101-0.401-0.118-0.136-0.0560.0570.0510.066-0.0980.0000.000
avg_val_received0.2430.019-0.0220.5940.9371.0000.4270.6820.7040.6490.7920.187-0.269-0.236-0.099-0.087-0.230-0.234-0.343-0.238-0.248-0.133-0.099-0.103-0.089-0.2310.0000.000
min_val_sent0.062-0.336-0.2840.7730.3010.4271.0000.6000.7140.5550.086-0.210-0.544-0.509-0.402-0.401-0.515-0.516-0.068-0.481-0.482-0.272-0.402-0.402-0.402-0.5150.0000.000
max_val_sent0.575-0.070-0.0690.7290.6750.6820.6001.0000.9730.9670.521-0.197-0.260-0.2380.1460.155-0.244-0.245-0.411-0.225-0.2430.0020.1460.1410.154-0.2430.0000.000
avg_val_sent0.472-0.149-0.1430.7890.6570.7040.7140.9731.0000.9370.482-0.195-0.340-0.3170.0270.037-0.319-0.321-0.361-0.300-0.315-0.0260.0280.0250.035-0.3190.0000.000
total_Ether_sent0.6260.0130.0110.6850.6490.6490.5550.9670.9371.0000.553-0.157-0.261-0.2410.1580.170-0.243-0.244-0.440-0.238-0.256-0.0100.1580.1520.169-0.2420.0000.000
total_ether_received0.3050.3160.3010.2430.8850.7920.0860.5210.4820.5531.0000.466-0.022-0.0690.0570.0740.004-0.001-0.377-0.101-0.115-0.0500.0580.0510.0710.0020.0000.000
total_ether_balance0.0150.4800.441-0.1960.2590.187-0.210-0.197-0.195-0.1570.4661.0000.1400.052-0.200-0.2080.1510.156-0.0890.0110.0110.000-0.199-0.191-0.2060.1560.0000.000
Total_ERC20_tnxs0.0970.4730.577-0.607-0.118-0.269-0.544-0.260-0.340-0.261-0.0220.1401.0000.9330.5330.5350.9760.9770.2080.9160.8990.3170.5330.5310.5360.9770.0000.000
ERC20_total_Ether_received0.1330.4160.447-0.543-0.119-0.236-0.509-0.238-0.317-0.241-0.0690.0520.9331.0000.4950.4900.9370.9420.3430.9900.9880.2510.4950.4940.4920.9450.0000.000
ERC20_total_ether_sent0.3770.2630.312-0.1890.057-0.099-0.4020.1460.0270.1580.057-0.2000.5330.4951.0000.9980.4010.410-0.2210.4980.4910.7081.0000.9990.9980.4120.0000.000
ERC20_uniq_sent_addr0.3790.2650.321-0.1880.068-0.087-0.4010.1550.0370.1700.074-0.2080.5350.4900.9981.0000.4040.411-0.2240.4920.4840.7130.9970.9960.9990.4130.0000.000
ERC20_uniq_rec_addr0.0920.4730.574-0.596-0.099-0.230-0.515-0.244-0.319-0.2430.0040.1510.9760.9370.4010.4041.0000.9980.2370.9180.9000.1780.4010.3970.4040.9980.0000.000
ERC20_uniq_rec_contract_addr0.0960.4790.572-0.595-0.101-0.234-0.516-0.245-0.321-0.244-0.0010.1560.9770.9420.4100.4110.9981.0000.2300.9240.9060.1750.4100.4070.4131.0000.0000.000
ERC20_min_val_rec-0.303-0.110-0.094-0.343-0.401-0.343-0.068-0.411-0.361-0.440-0.377-0.0890.2080.343-0.221-0.2240.2370.2301.0000.3510.390-0.117-0.221-0.219-0.2250.2290.0000.000
ERC20_max_val_rec0.1390.3790.407-0.519-0.118-0.238-0.481-0.225-0.300-0.238-0.1010.0110.9160.9900.4980.4920.9180.9240.3511.0000.9960.2540.4980.4970.4950.9260.0000.000
ERC20_avg_val_rec0.1180.3650.386-0.517-0.136-0.248-0.482-0.243-0.315-0.256-0.1150.0110.8990.9880.4910.4840.9000.9060.3900.9961.0000.2500.4920.4920.4870.9090.0000.000
ERC20_min_val_sent0.1670.0550.105-0.135-0.056-0.133-0.2720.002-0.026-0.010-0.0500.0000.3170.2510.7080.7130.1780.175-0.1170.2540.2501.0000.7080.7170.7010.1760.0000.000
ERC20_max_val_sent0.3760.2610.312-0.1880.057-0.099-0.4020.1460.0280.1580.058-0.1990.5330.4951.0000.9970.4010.410-0.2210.4980.4920.7081.0001.0000.9980.4120.0000.000
ERC20_avg_val_sent0.3740.2600.307-0.1880.051-0.103-0.4020.1410.0250.1520.051-0.1910.5310.4940.9990.9960.3970.407-0.2190.4970.4920.7171.0001.0000.9970.4090.0000.000
ERC20_uniq_sent_token_name0.3810.2690.323-0.1870.066-0.089-0.4020.1540.0350.1690.071-0.2060.5360.4920.9980.9990.4040.413-0.2250.4950.4870.7010.9980.9971.0000.4140.0000.000
ERC20_uniq_rec_token_name0.0980.4790.569-0.593-0.098-0.231-0.515-0.243-0.319-0.2420.0020.1560.9770.9450.4120.4130.9981.0000.2290.9260.9090.1760.4120.4090.4141.0000.0000.000
ERC20_total_Ether_sent_contract0.0000.0000.4810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.485
ERC20_uniq_sent_addr.10.0000.0000.4810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4851.000

Missing values

2023-04-10T19:12:21.787410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-10T19:12:23.069633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-10T19:12:24.162613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Avg_min_between_sent_tnxAvg_min_between_received_tnxTime_Diff_between_first_and_last_(Mins)min_value_receivedmax_value_receivedavg_val_receivedmin_val_sentmax_val_sentavg_val_senttotal_Ether_senttotal_ether_receivedtotal_ether_balanceTotal_ERC20_tnxsERC20_total_Ether_receivedERC20_total_ether_sentERC20_total_Ether_sent_contractERC20_uniq_sent_addrERC20_uniq_rec_addrERC20_uniq_sent_addr.1ERC20_uniq_rec_contract_addrERC20_avg_time_between_sent_tnxERC20_avg_time_between_rec_tnxERC20_avg_time_between_rec_2_tnxERC20_avg_time_between_contract_tnxERC20_min_val_recERC20_max_val_recERC20_avg_val_recERC20_min_val_sentERC20_max_val_sentERC20_avg_val_sentERC20_min_val_sent_contractERC20_max_val_sent_contractERC20_avg_val_sent_contractERC20_uniq_sent_token_nameERC20_uniq_rec_token_name
235121.650000.0000064.95000101.00000101.00000101.000001.0000091.1874033.66615100.99845101.000000.001550.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000
33140.500000.00000281.000005.000005.000005.000002.496942.500002.498474.996945.000000.003061.0000013.370000.000000.000000.000001.000000.000001.000000.000000.000000.000000.0000013.3700013.3700013.370000.000000.000000.000000.000000.000000.000000.000001.00000
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41337133.4100031214.68000378118.770000.00100187.0600033.828490.00000187.2530015.04739270.85300270.62790-0.2251022.000001141890.121001141889.490000.000007.000007.000000.000006.000000.000000.000000.000000.000000.00000342426.6000095157.510100.00000342426.00000114188.949000.000000.000000.000005.000006.00000
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202412.9300017.8200061.480000.012350.081310.046830.000000.000000.000000.000000.093660.093663.000007812.000007812.000000.000001.000001.000000.000001.000000.000000.000000.000000.000007812.000007812.000007812.000002812.000005000.000003906.000000.000000.000000.000001.000001.00000
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4970.00000157.810006154.770000.0307318.000001.489780.000000.000000.000000.0000058.1014058.101406.0000015105.337000.000000.000000.000006.000000.000006.000000.000000.000000.000000.000000.0000015000.000002517.556170.000000.000000.000000.000000.000000.000000.000006.00000
3313107.700000.82000324.7300011.8610289.1389850.500002.0000087.1984433.66615100.99844101.000000.001560.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000
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Avg_min_between_sent_tnxAvg_min_between_received_tnxTime_Diff_between_first_and_last_(Mins)min_value_receivedmax_value_receivedavg_val_receivedmin_val_sentmax_val_sentavg_val_senttotal_Ether_senttotal_ether_receivedtotal_ether_balanceTotal_ERC20_tnxsERC20_total_Ether_receivedERC20_total_ether_sentERC20_total_Ether_sent_contractERC20_uniq_sent_addrERC20_uniq_rec_addrERC20_uniq_sent_addr.1ERC20_uniq_rec_contract_addrERC20_avg_time_between_sent_tnxERC20_avg_time_between_rec_tnxERC20_avg_time_between_rec_2_tnxERC20_avg_time_between_contract_tnxERC20_min_val_recERC20_max_val_recERC20_avg_val_recERC20_min_val_sentERC20_max_val_sentERC20_avg_val_sentERC20_min_val_sent_contractERC20_max_val_sent_contractERC20_avg_val_sent_contractERC20_uniq_sent_token_nameERC20_uniq_rec_token_name
690.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
40430.0000011682.08000105138.730000.00000100.0000026.688890.000000.000000.000000.00000240.20000240.200002.00000603.766310.000000.000000.000002.000000.000002.000000.000000.000000.000000.000003.76632600.00000301.883160.000000.000000.000000.000000.000000.000000.000002.00000
225122.180000.0000066.55000101.00000101.00000101.000003.4686165.0497233.66615100.99845101.000000.001550.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000
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14130.00000135.210004724.030000.062711.482640.533535.867925.867925.867925.867925.868780.000861.000001.337000.000000.000000.000001.000000.000001.000000.000000.000000.000000.000001.337001.337001.337000.000000.000000.000000.000000.000000.000000.000001.00000
1168143.2700095.030001096.500000.500001.000000.672610.500000.688000.537602.688002.690420.00242NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14510.000000.000002398.670000.100000.100000.100000.096880.096880.096880.096880.100000.00312NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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Duplicate rows

Most frequently occurring

Avg_min_between_sent_tnxAvg_min_between_received_tnxTime_Diff_between_first_and_last_(Mins)min_value_receivedmax_value_receivedavg_val_receivedmin_val_sentmax_val_sentavg_val_senttotal_Ether_senttotal_ether_receivedtotal_ether_balanceTotal_ERC20_tnxsERC20_total_Ether_receivedERC20_total_ether_sentERC20_total_Ether_sent_contractERC20_uniq_sent_addrERC20_uniq_rec_addrERC20_uniq_sent_addr.1ERC20_uniq_rec_contract_addrERC20_avg_time_between_sent_tnxERC20_avg_time_between_rec_tnxERC20_avg_time_between_rec_2_tnxERC20_avg_time_between_contract_tnxERC20_min_val_recERC20_max_val_recERC20_avg_val_recERC20_min_val_sentERC20_max_val_sentERC20_avg_val_sentERC20_min_val_sent_contractERC20_max_val_sent_contractERC20_avg_val_sent_contractERC20_uniq_sent_token_nameERC20_uniq_rec_token_name# duplicates
20.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5
00.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000001.000001.337000.000000.000000.000001.000000.000001.000000.000000.000000.000000.000001.337001.337001.337000.000000.000000.000000.000000.000000.000000.000001.000003
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